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Intro to Machine Learning with R & caret
 
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Lecture starts at 3:00 The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package (short for [C]lassification [A]nd [RE]gression [T]raining) is a must-have tool in your toolbelt. The caret package provides capabilities that are ubiquitous in all stages of the data science project lifecycle. Most important of all, caret provides a common interface for training, tuning, and evaluating more than 200 machine learning algorithms. Not surprisingly, caret is a sure fire way to accelerate your velocity as a data scientist! In this presentation Dave Langer will provide an introduction to the caret package. The focus of the presentation will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work. Attendees will learn how to: • Create stratified random samples of data useful for training machine learning models. • Train machine learning models using caret’s common interface. • Leverage caret’s powerful features for cross-validation and hyperparameter tuning. • Scale caret via use of multi-core, parallel training. • Increase their knowledge of caret’s many features. R code and accompanying dataset: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Machine%20Learning%20with%20R%20and%20Caret caret website: http://topepo.github.io/caret/index.html Learn more about David here: https://www.meetup.com/data-science-dojo/events/239730653/ -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ from over 742 companies globally. This channel contains tutorials, community talks, and courses on data science and data engineering. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8wHn0 See what our past attendees are saying here: https://hubs.ly/H0f8wtJ0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 42088 Data Science Dojo
Practical Machine Learning - Caret Package
 
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This video is under a Creative Commons Attribution - Noncommercial - Share Alike license (CC-BY-NC-SA)
Views: 3251 Open Education Lab
caret package webinar
 
01:09:38
Presented by Max Kuhn for the Orange County R User Group. Organized and recorded by Ray DiGiacomo, Jr. (President, OC-RUG, [email protected])
Views: 51468 Max Kuhn
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 46886 edureka!
Tutorial R Caret:  Modelos Basicos de Prediccion
 
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Este video muestra como estimar los modelos basicos de prediccion (regresion lineal, knn, arboles de regresion) usando el library caret de R.
Views: 978 dataminingincae
Implementing k-nearest neighbour with caret (Machine Learning with R)
 
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Implementing k-nearest neighbour with caret (Machine Learning with R)
Naive Bayes Classification with R | Example with Steps
 
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Provides steps for applying Naive Bayes Classification with R. Data: https://goo.gl/nCFX1x R file: https://goo.gl/Feo5mT Machine Learning videos: https://goo.gl/WHHqWP Naive Bayes Classification is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 20007 Bharatendra Rai
R  - Regression Trees - CART
 
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Regression Trees are part of the CART family of techniques for prediction of a numerical target feature. Here we use the package rpart, with its CART algorithms, in R to learn a regression tree model on the msleep' data set available in the ggplot2 package.
Views: 39775 Jalayer Academy
Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Logistic Regression Tutorial shall give you a clear understanding as to how a Logistic Regression machine learning algorithm works in R. Towards the end, in our demo we will be predicting which patients have diabetes using Logistic Regression! In this Logistic Regression Tutorial video you will understand: 1) The 5 Questions asked in Data Science 2) What is Regression? 3) Logistic Regression - What and Why? 4) How does Logistic Regression Work? 5) Demo in R: Diabetes Use Case 6) Logistic Regression: Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 85515 edureka!
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 59181 Bharatendra Rai
Comparing Regression Models for caret : Boston Housing
 
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Comparing Regression Models for caret : Boston Housing
Views: 1205 Dragonfly Statistics
Decision Tree with R | Complete Example
 
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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 54090 Bharatendra Rai
R tutorial: Cross-validation
 
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Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox In the last video, we manually split our data into a single test set, and evaluated out-of-sample error once. However, this process is a little fragile: the presence or absence of a single outlier can vastly change our out-of-sample RMSE. A better approach than a simple train/test split is using multiple test sets and averaging out-of-sample error, which gives us a more precise estimate of true out-of-sample error. One of the most common approaches for multiple test sets is known as "cross-validation", in which we split our data into ten "folds" or train/test splits. We create these folds in such a way that each point in our dataset occurs in exactly one test set. This gives us 10 test sets, and better yet, means that every single point in our dataset occurs exactly once. In other words, we get a test set that is the same size as our training set, but is composed of out-of-sample predictions! We assign each row to its single test set randomly, to avoid any kind of systemic biases in our data. This is one of the best ways to estimate out-of-sample error for predictive models. One important note: after doing cross-validation, you throw all resampled models away and start over! Cross-validation is only used to estimate the out-of-sample error for your model. Once you know this, you re-fit your model on the full training dataset, so as to fully exploit the information in that dataset. This, by definition, makes cross-validation very expensive: it inherently takes 11 times as long as fitting a single model (10 cross-validation models plus the final model). The train function in caret does a different kind of re-sampling known as bootsrap validation, but is also capable of doing cross-validation, and the two methods in practice yield similar results. Lets fit a cross-validated model to the mtcars dataset. First, we set the random seed, since cross-validation randomly assigns rows to each fold and we want to be able to reproduce our model exactly. The train function has a formula interface, which is identical to the formula interface for the lm function in base R. However, it supports fitting hundreds of different models, which are easily specified with the "method" argument. In this case, we fit a linear regression model, but we could just as easily specify method = 'rf' and fit a random forest model, without changing any of our code. This is the second most useful feature of the caret package, behind cross-validation of models: it provides a common interface to hundreds of different predictive models. The trControl argument controls the parameters caret uses for cross-validation. In this course, we will mostly use 10-fold cross-validation, but this flexible function supports many other cross-validation schemes. Additionally, we provide the verboseIter = TRUE argument, which gives us a progress log as the model is being fit and lets us know if we have time to get coffee while the models run. Let's practice cross-validating some models.
Views: 43954 DataCamp
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
01:07:14
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 57610 edureka!
Machine Learning Real-time - Stock Prediction Application using Shiny & R
 
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Real-time Scenarios - Stock Prediction Application Data Science & Machine Learning Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Get the Code here Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori
Views: 24320 BharatiDWConsultancy
Webinar Machine Learning using Caret Package
 
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Data science Webinar on Machine Learning with Caret Package --------------------------------------------------------------------------------------------------------- Our videos: Webinar on Regularization Lasso & Ridge https://goo.gl/ATthWo Semi-supervised learning https://goo.gl/RHC4UW Types of Custom Charts in Tableau https://goo.gl/ihw3vU Resume Preparation session https://goo.gl/JUDuiR Webinar on Survival Analysis https://goo.gl/JUDuiR Human Resources Management In a Project https://goo.gl/VK7wge End-to-End handling of a Data Science Project https://goo.gl/MV8EZx Business Analytics https://goo.gl/tuwy4h Exploratory Data Analysis (EDA) using R https://goo.gl/vaS7Lw ` ` Please follow us on social media (links provided below) https://www.facebook.com/innodatatics/ https://www.linkedin.com/company/innodatatics https://www.instagram.com/innodatatics/ https://in.pinterest.com/innodatatics/ https://twitter.com/innodatatics https://plus.google.com/100770952954306916011?hl=en
Views: 202 Innodatatics Inc
Ridge, Lasso & Elastic Net Regression with R | Boston Housing Data Example,  Steps & Interpretation
 
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Provides example with interpretations of applying Ridge, Lasso & Elastic Net Regression using Boston Housing data. R file: https://goo.gl/ywtVYg Machine Learning videos: https://goo.gl/WHHqWP Includes. - example with Boston housing data - illustrates use of caret package - data partition - custom control parameters - cross validation - linear model - residuals plot - use of glmnet package - ridge regression - plot results - log lambda plot - fraction deviance explained plot - variable importance plot - interpretation - lasso regression - elastic net regression - compare models - best model - saving and reading final model for later use - prediction R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 13532 Bharatendra Rai
Feature Selection Using R
 
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Provides steps for carrying out feature selection for building machine learning models using Boruta package. R code: https://goo.gl/h46Rv2 More ML videos: https://goo.gl/WHHqWP Feature selection is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 4196 Bharatendra Rai
Reducing High Dimensional Data with PCA and prcomp: ML with R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu In this R video, we'll see how PCA can reduce a 1000+ variable data set into 10 variables and barely lose accuracy! Walkthrough & code: http://amunategui.github.io/high-demensions-pca/ Note: data source url in the video no longer works, see the walkthrough for new source: http://amunategui.github.io/high-demensions-pca/ Note: for those that can't use xgboost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/high-demensions-pca/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" This has been re-designed as 'Reducing High Dimensional Data in R' on Udemy.com, $19 COUPON!!!: https://www.udemy.com/practical-data-science-reducing-high-dimensional-data-in-r/?couponCode=1111 Check out my other in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 39982 Manuel Amunategui
R tutorial: Data splitting and confusion matrices
 
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Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r We have seen several techniques for preprocessing the data. When the data is fully preprocessed, you can go ahead and start your analysis. You can run the model on the entire data set, and use the same data set for evaluating the result, but this will most likely lead to a result that is too optimistic. One alternative is to split the data into two pieces. The first part of the data, the so-called training set, can be used for building the model and the second part of the data, the test set, can be used to test the results. One common way of doing this is to use two-thirds of the data for a training set and one-third of the data for the test set. Of course there can be a lot of variation in the performance estimate depending which two-thirds of the data you select for the training set. One way to reduce this variation is by using cross validation. For the two-thirds training set and one-third test set example, a cross validation variant would look like this. The data would be split in three equal parts, and each time, two of these parts would act as a training set, and one part would act as a test set. Of course, we could use as many parts as we want, but we would have to run the model many times if using many parts. This may become computationally heavy. In this course, we will just use one training set and one test set containing two-thirds versus one-third of the data, respectively. Imagine we have just run a model, and now we apply the model to our test set to see how good the results are. Evaluating the model for credit risk means comparing the observed outcomes of default versus non-default--stored in the loan_status variable of the test set--with the predicted outcomes according to the model. If we are dealing with a large number of predictions, a popular method for summarizing the results uses something called a confusion matrix. Here, we use just 14 values to demonstrate the concept. A confusion matrix is a contingency table of correct and incorrect classifications. Correct classifications are on the diagonal of the confusion matrix. We see, for example, that 8 non-defaulters were correctly classified as non-default, and 3 defaulters were correctly classified as defaulters. However, we see that 2 non-defaulters where wrongly classified as defaulters, and 1 defaulter was wrongly classified as a non-defaulter. The items on the diagonals are also called the true positives and true negatives. The off-diagonals are called the false positives versus the false negatives. Several measures can be derived from the confusion matrix. We will discuss the classification accuracy, the sensitivity and the specificity. The classification accuracy is the percentage of correctly classified instances, which is equal to 78.57% in this example. The sensitivity is the percentage of good customers that are classified correctly, or 75% in this example. The specificity is the percentage of bad costomers that are classified correctly, or 0.80 in this example. Let's practice splitting the data and constructing confusion matrices.
Views: 13994 DataCamp
Handling Class Imbalance Problem in R: Improving Predictive Model Performance
 
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Provides steps for carrying handling class imbalance problem when developing classification and prediction models Download R file: https://goo.gl/ns7zNm data: https://goo.gl/d5JFtq Includes, - What is Class Imbalance Problem? - Data partitioning - Data for developing prediction model - Developing prediction model - Predictive model evaluation - Confusion matrix, - Accuracy, sensitivity, and specificity - Oversampling, undersampling, synthetic sampling using random over sampling examples predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 14136 Bharatendra Rai
Support Vector Machine (SVM) with R - Classification and Prediction Example
 
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Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 38110 Bharatendra Rai
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 29867 Tatvic Analytics
K-Nearest Neighbour (KNN) with R | Classification and Regression Examples
 
20:39
Provides concepts and steps for applying knn algorithm for classification and regression problems. R code: https://goo.gl/FqpxWK Data file: https://goo.gl/D2Asm7 More ML videos: https://goo.gl/WHHqWP R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 5514 Bharatendra Rai
Introduction to Event Log Mining with R
 
01:39:08
Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things (IoT) architectures. Even Enterprise Resource Planning (ERP) systems produce event logs! Given the rich and varied data contained in event logs, mining these assets is a critical skill needed by every Data Scientist, Business/Data Analyst, and Program/Product Manager. At this meetup, presenter Dave Langer, will show how easy it is to get started mining your event logs using the OSS tools of R and ProM. Dave will cover the following during the presentation: • The scenarios and benefits of event log mining • The minimum data required for event log mining • Ingesting and analyzing event log data using R • Process Mining with ProM • Event log mining techniques to create features suitable for Machine Learning models • Where you can learn more about this very handy set of tools and techniques *R source code will be made available via GitHub here: https://github.com/EasyD/IntroToEventLogMiningMeetup Find out more about David here: https://www.meetup.com/data-science-dojo/events/235913034/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8y2K0 See what our past attendees are saying here: https://hubs.ly/H0f8xNz0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 6654 Data Science Dojo
Machine Learning with R | Machine Learning Algorithms | Data Science Training | Edureka
 
40:36
( Data Science Training : https://www.edureka.co/data-science ) This "Machine Learning with R" video by Edureka will help you to understand the core concepts of Machine Learning followed by a very interesting case study on Pokemon Dataset in R. This tutorial will comprise of these topics: 1. Understanding Machine Learning 2. Applications of Machine Learning 3. Types of Machine Learning Algorithms 4. Case Study on the "Pokemon Dataset" to implement Machine Learning Algorithms Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 23761 edureka!
Bagging & Ensemble Models| Bootstrap aggregation | Data Science in R
 
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decision Tree, though a good model to interpret the outcomes, not a good model in terms of prediction accuracy. Bagging is a combination of several decision trees that helps in improving the prediction accuracy of decision tree model. It can be used for both regression & classification problems Contact :[email protected] ANalytics Study Pack : https://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 6656 Analytics University
Data Pre processing
 
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For more see: http://shishirshakya.blogspot.com/2015/08/data-pre-processing.html
Views: 2081 Shishir Shakya
Modeling Ensembles using the Caret Package: Machine Learning With R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks! Simple way to run ensembles and blend the probabilities by adding them to a final 'blender' model. Code and walkthrough: http://amunategui.github.io/blending-models/
Views: 16314 Manuel Amunategui
Introduction to Data Science with R - Cross Validation
 
01:00:34
Part 5 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 50871 David Langer
Confusion Matrices - Machine Learning with caret
 
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Confusion Matrices - Machine Learning with caret
Views: 4046 Dragonfly Statistics
Decision Tree Classification in R
 
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This video covers how you can can use rpart library in R to build decision trees for classification. The video provides a brief overview of decision tree and the shows a demo of using rpart to create decision tree models, visualise it and predict using the decision tree model
Views: 76056 Melvin L
Walkthrough of the dummyVars function from the {caret} package: Machine Learning with R
 
11:00
Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 8870 Manuel Amunategui
R: Applied Predictive Modeling
 
01:22:41
For more workshops, please visit: http://scientistcafe.com. For future workshops, you can follow twitter: @gossip_rabbit or join our meetup group: http://www.meetup.com/Central-Iowa-R-User-Group/ Max Kuhn, author of Applied Predictive Modeling (http://appliedpredictivemodeling.com/) and caret package, will talk about the practice of predictive modeling. The practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model's prediction accuracy on future data.
Views: 9299 Hui Lin
Neural Networks in R: Example with Categorical Response at Two Levels
 
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Provides steps for applying artificial neural networks to do classification and prediction. R file: https://goo.gl/VDgcXX Data file: https://goo.gl/D2Asm7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - neural network model - input, hidden, and output layers - min-max normalization - prediction - confusion matrix - misclassification error - network repetitions - example with binary data neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 26451 Bharatendra Rai
Feature Selection in Machine learning| Variable selection| Dimension Reduction
 
56:25
Feature selection is an important step in machine learning model building process. The performance of models depends in the following : Choice of algorithm Feature Selection Feature Creation Model Selection So feature selection is one important reason for good performance. They are primarily of three types: Filter Methods Wrapper Methods Embedded Methods You will learn a number of techniques such as variable selection through Correlation matrix, subset selection, stepwise forward, stepwise backward, hybrid method etc. You will also learn regularization (shrinkage) methods such as lasso and Ridge regression that can well be used for variable selection. Finally you will learn difference between variable selection and dimension reduction ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 25949 Analytics University
Support Vector Machine in R | SVM Algorithm Example | Data Science With R Tutorial | Simplilearn
 
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This Support Vector Machine in R tutorial video will help you understand what is Machine Learning, what is classification, what is Support Vector Machine (SVM), what is SVM kernel and you will also see a use case in which we will classify horses and mules from a given data set using SVM algorithm. SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph. SVM is a classification algorithm used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Now, let us get started and understand Support Vector Machine in detail. Below topics are explained in this "Support Vector Machine in R" video: 1. What is machine learning? 2. What is classification? 3. What is support vector machine? 4. Understanding support vector machine 5. Understanding SVM kernel 6. Use case: classifying horses and mules To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/w72XBR Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Support-Vector-Machine-in-R-QkAmOb1AMrY&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 6954 Simplilearn
Neural Networks in R | Arpan Gupta | Data Scientist & IITian
 
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Here I will explain Neural networks in R for Machine learning working,how to fit a machine learning model like neural network in R,plotting neural network for machine learning in R,predictions using neural network in R.neuralnet package is used for this modelling.Also I have described the basic Machine learning modelling procedure in R.Its a neural network tutorial for Machine Learning . #neuralnetwork #machinelearning #datascience #R
Using Correlations To Understand Your Data: Machine Learning With R
 
11:56
Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu A great way to explore new data is to use a pairwise correlation matrix. This will pair every combination of your variables and measure the correlation between them. Code and walkthrough: http://amunategui.github.io/Exploring-Your-Data-Set/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 52183 Manuel Amunategui
Max Kuhn: Applied Predictive Modeling
 
01:54:59
Max Kuhn, Director is Nonclinical Statistics of Pfizer and also the author of Applied Predictive Modeling. Max is a nonclinical statistician who has been applying predictive models in the diagnostic and pharmaceutical industries for over 15 years. He is the author and maintainer for a number of predictive modeling packages, including caret, C50, Cubist and AppliedPredictiveModeling.
R tutorial: Machine learning toolbox
 
02:13
Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox Welcome to the machine learning toolbox course. I'm Max Kuhn, statistician and author of the caret package, which I've been working on for over a decade. Today caret is one of the most widely used packages in R for supervised learning (also known as predictive modeling). Supervised learning is machine learning when you have a "target variable," or something specific you want to predict. A classic example of supervised learning is predicting which species an iris is, based on its physical measurements. Another example would be predicting which customers in your business will "churn" or cancel their service. In both of these cases, we have something specific we want to predict on new data: species and churn. There are two main kinds of predictive models: classification and regression. Classification models predict qualitative variables, for example the species of a flower, or "will a customer churn". Regression models predict quantitative variables, for example the price of a diamond. Once we have a model, we use a "metric" to evaluate how well the model works. A metric is quantifiable and gives us an objective measure of how well the model predicts on new data. For regression problems, we will focus on "root mean squared error" or RMSE as our metric of choice. This is the error that linear regression models typically seek to minimize, for example in the lm() function in R. It's a good, general purpose error metric, and the most common one for regression models. Unfortunately, it's common practice to calculate RMSE on the same data we used to fit the model. This typically leads to overly-optimistic estimates of model performance. This is also known as overfitting. A better approach is to use out-of-sample estimates of model performance. This is the approach caret takes, because it simulates what happens in the real world and helps us avoid over-fitting. However, it's useful to start off by looking at in-sample error, so we can contrast it later with out-of-sample error on the same dataset. First, we load the mtcars dataset and fit a model to the first 20 rows. Next, we make in-sample predictions, using the predict function on our model. Finally, we calculate RMSE on our training data, and get pretty good results. Let's practice calculating RMSE on some other datasets.
Views: 2056 DataCamp
KNN Algorithm Using R | KNN Algorithm Example | Data Science Training | Edureka
 
24:59
** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka video on "KNN algorithm using R", will help you learn about the KNN algorithm in depth, you'll also see how KNN is used to solve real-world problems. Below are the topics covered in this module: (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #knn #datasciencewithr #datasciencecourse #datascienceforbeginners #knnalgorithm #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyze Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyze data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 3461 edureka!
Missing Value - kNN imputation in R
 
10:48
This video discusses about how to do kNN imputation in R for both numerical and categorical variables.
Views: 21799 Gourab Nath
Ensemble learners
 
02:52
This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 46119 Udacity
Data Mining with Weka (4.6: Ensemble learning)
 
10:00
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Ensemble learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 22289 WekaMOOC
Doing predictive modeling using R - Rattle (Togaware)
 
02:11:19
This session covers equivalent of all SAS procedures using free software - R Rattle. Hypothesis testing, Linear and Logistic regression, Cluster Analysis. Introduction to Random Forests, SVM, Boosting etc. www.learnanalytics.in
Views: 26122 Learn Analytics
Introduction to Text Analytics with R: SVD with R
 
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SVD with R includes specific coverage of: – Use of the irlba package to perform truncated SVD. – How to project a TF-IDF document vector into the SVD semantic space (i.e., LSA). – Comparison of model performance between a single decision tree and the mighty random forest. – Exploration of random forest tuning using the caret package. About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K5H0 See what our past attendees are saying here: https://hubs.ly/H0f5JTc0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 8745 Data Science Dojo
Tutorial R Caret: Modelos Basicos de Clasificacion
 
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En este tutorial se muestra como estimar modelos basicos de clasificacion (regresion logistica, knn, arboles) usando el library caret de R. Datos: "https://s3.amazonaws.com/mirlitus/bwt.csv"
Views: 240 dataminingincae
Four Types Of Cross Validation| K-Fold | Leave One Out |Bootstrap | Hold Out
 
20:58
In this video you will learn about the different types of cross validation you can use to validate you statistical model. Cross validation is an important step in model building which ensures you have a model that will perform well in the new data , which also overcomes the possibility model being over fit. There are four types of cross validation you will learn 1- Hold out Method 2- K-Fold CV 3- Leave one out CV 4-Bootstrap Methods for more learn here : https://www.cs.cmu.edu/~schneide/tut5/node42.html Cross validation is also important step in machine learning model building and data science model building. Study Packs : https://analyticuniversity.com Facebook : https://www.facebook.com/AnalyticsUniversity Twitter : https://twitter.com/AnalyticsUniver
Views: 39671 Analytics University